Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generation of synthetic data sets for evaluating the accuracy of knowledge discovery systems
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Genetic-Based Synthetic Data Sets for the Analysis of Classifiers Behavior
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Data Complexity Analysis: Linkage between Context and Solution in Classification
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Adaptive artificial datasets through learning classifier systems for classification tasks
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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One of the most important challenges in supervised learning is how to evaluate the quality of the models evolved by different machine learning techniques. Up to now, we have relied on measures obtained by running the methods on a wide test bed composed of real-world problems. Nevertheless, the unknown inherent characteristics of these problems and the bias of learners may lead to inconclusive results. This paper discusses the need to work under a controlled scenario and bets on artificial data set generation. A list of ingredients and some ideas about how to guide such generation are provided, and promising results of an evolutionary multi-objective approach which incorporates the use of data complexity estimates are presented.